I recently concluded a piece of research into the employment implications of South Africa’s power sector transition; a literature review. The objective of the study (undertaken for Meridian Economics and commissioned by South African Wind Energy Association) was to assess the current status of information on this current and highly politicised topic.
I was warned early on in the process by a stakeholder in the field that I was entering ‘murky’ waters. How right he was! As I read, my mind boggled at the complexity. I grappled to identify its source/s. Some of the complexity was tied up in the different agendas and perspectives operating within the field. Some was related to methods and metrics used. Timeframes provided an additional source, as did the use of language.
I was aware that being able to deliver a table comparing numbers that different studies put to the employment creation associated with different power generation technologies would have been a coup, and perhaps what was hoped for of my research. But I was utterly unable to do this within any reasonable timeframe and with any integrity. The columns in my spreadsheet accounting for the relevant dimensions of each study (method, data, timeframe, scale, technologies, metric etc) ran to ‘w’ and I wasn’t done.
Socio-techno-economic systems such as the South African power sector are clearly infinitely complex, with interconnections and causal drivers operating at multiple levels. Further, complexity theories hold that the future is fundamentally unknowable, with deep uncertainty a basic assumption. Yet this doesn’t imply that we can stop at the finding of ‘its complex’ when attempting to understand them better. (Although how I wished the theory did justify taking this route at many points in the research!)
In grappling with the sources of complexity, I reflected that they could perhaps be usefully divided into two types. The first is a lack of clarity or uniformity relating to the metrics used and the methods employed – although perhaps this is not true complexity but rather complication. The remedy for this is standardisation (as distinct from simplification), attention to underlying assumptions and a more responsible use of research findings than has been demonstrated in the press recently.
The second type of complexity sources is more inherent: the issue is just complex. And complexity theories warn against simplifying what is inherently complex, as doing so can mislead users of the research. Rather, complexity thinking holds that the challenge here is to reveal this complexity in a useful way. This means, perhaps paradoxically, more studies, more metrics, more methods and invoking a greater number of perspectives.
Reduce the complication and reveal the complexity. A simple take-away from a particularly complex piece of research!